Research Article

Genetic analysis of the age at menopause by using estimating equations and Bayesian random effects models

  1. K-A. Do1,2,*,
  2. B. M. Broom3,
  3. P. Kuhnert4,
  4. D. L. Duffy1,
  5. A. A. Todorov5,
  6. S. A. Treloar1,
  7. N. G. Martin1

Article first published online: 25 APR 2000

DOI: 10.1002/(SICI)1097-0258(20000515)19:9<1217::AID-SIM421>3.0.CO;2-Q

Issue

Statistics in Medicine

Statistics in Medicine

Volume 19, Issue 9, pages 1217–1235, 15 May 2000

Additional Information

How to Cite

Do, K.-A., Broom, B. M., Kuhnert, P., Duffy, D. L., Todorov, A. A., Treloar, S. A. and Martin, N. G. (2000), Genetic analysis of the age at menopause by using estimating equations and Bayesian random effects models. Statistics in Medicine, 19: 1217–1235. doi: 10.1002/(SICI)1097-0258(20000515)19:9<1217::AID-SIM421>3.0.CO;2-Q

Author Information

  1. 1

    Epidemiology and Population Health Unit, Queensland Institute of Medical Research, P.O. Royal Brisbane Hospital, Queensland 4029, Australia

  2. 2

    Department of Biostatistics, University of Texas, M. D. Anderson Cancer Center, Houston, Texas 77030, U.S.A.

  3. 3

    Center for Research in Parallel Computing, Rice University, 6100 Main Street, Houston, Texas 77005-1892, U.S.A.

  4. 4

    School of Mathematics, Queensland University of Technology, Brisbane, Queensland 4001, Australia.

  5. 5

    Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63108. U.S.A.

*Department of Biostatistics, University of Texas, M. D. Anderson Cancer Center, Houston, Texas 77030, U.S.A.

Publication History

  1. Issue published online: 25 APR 2000
  2. Article first published online: 25 APR 2000
  3. Manuscript Accepted:
  4. Manuscript Received:

Funded by

  • Australian National Health and Medical Research Council
  • NIH. Grant Numbers: AA07535, A07728

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Abstract

Multi-wave self-report data on age at menopause in 2182 female twin pairs (1355 monozygotic and 827 dizygotic pairs), were analysed to estimate the genetic, common and unique environmental contribution to variation in age at menopause. Two complementary approaches for analysing correlated time-to-onset twin data are considered: the generalized estimating equations (GEE) method in which one can estimate zygosity-specific dependence simultaneously with regression coefficients that describe the average population response to changing covariates; and a subject-specific Bayesian mixed model in which heterogeneity in regression parameters is explicitly modelled and the different components of variation may be estimated directly. The proportional hazards and Weibull models were utilized, as both produce natural frameworks for estimating relative risks while adjusting for simultaneous effects of other covariates. A simple Markov chain Monte Carlo method for covariate imputation of missing data was used and the actual implementation of the Bayesian model was based on Gibbs sampling using the freeware package BUGS. Copyright © 2000 John Wiley & Sons, Ltd.

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